Microarray expression matrix platform GPL6106 and clinical data for 67 septicemic patients and made them available as GEO accession GSE13015. GSE13015 data have been parsed into a SummarizedExperiment object available in ExperimentHub can be used for Differential Expression Analysis, Modular repertiore analysis.
In the below example, we show how one can download this dataset from ExperimentHub.
library(ExperimentHub)
## Loading required package: BiocGenerics
##
## Attaching package: 'BiocGenerics'
## The following objects are masked from 'package:stats':
##
## IQR, mad, sd, var, xtabs
## The following objects are masked from 'package:base':
##
## Filter, Find, Map, Position, Reduce, anyDuplicated, aperm, append,
## as.data.frame, basename, cbind, colnames, dirname, do.call,
## duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
## lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
## pmin.int, rank, rbind, rownames, sapply, saveRDS, setdiff, table,
## tapply, union, unique, unsplit, which.max, which.min
## Loading required package: AnnotationHub
## Loading required package: BiocFileCache
## Loading required package: dbplyr
dat = ExperimentHub()
hub = query(dat , "GSE13015")
temp = hub[["EH5429"]]
## see ?GSE13015 and browseVignettes('GSE13015') for documentation
## loading from cache
## require("SummarizedExperiment")
sessionInfo()
## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.20-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] SummarizedExperiment_1.36.0 GenomicRanges_1.58.0
## [3] GenomeInfoDb_1.42.0 IRanges_2.40.0
## [5] S4Vectors_0.44.0 MatrixGenerics_1.18.0
## [7] matrixStats_1.4.1 GSE13015_1.14.0
## [9] GEOquery_2.74.0 Biobase_2.66.0
## [11] ExperimentHub_2.14.0 AnnotationHub_3.14.0
## [13] BiocFileCache_2.14.0 dbplyr_2.5.0
## [15] BiocGenerics_0.52.0 BiocStyle_2.34.0
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.2.1 dplyr_1.1.4 blob_1.2.4
## [4] filelock_1.0.3 Biostrings_2.74.0 fastmap_1.2.0
## [7] XML_3.99-0.17 digest_0.6.37 mime_0.12
## [10] lifecycle_1.0.4 statmod_1.5.0 KEGGREST_1.46.0
## [13] RSQLite_2.3.7 magrittr_2.0.3 compiler_4.4.1
## [16] rlang_1.1.4 sass_0.4.9 tools_4.4.1
## [19] utf8_1.2.4 yaml_2.3.10 data.table_1.16.2
## [22] knitr_1.48 S4Arrays_1.6.0 bit_4.5.0
## [25] curl_5.2.3 DelayedArray_0.32.0 xml2_1.3.6
## [28] abind_1.4-8 withr_3.0.2 purrr_1.0.2
## [31] grid_4.4.1 preprocessCore_1.68.0 fansi_1.0.6
## [34] cli_3.6.3 rmarkdown_2.28 crayon_1.5.3
## [37] generics_0.1.3 httr_1.4.7 tzdb_0.4.0
## [40] DBI_1.2.3 cachem_1.1.0 zlibbioc_1.52.0
## [43] AnnotationDbi_1.68.0 BiocManager_1.30.25 XVector_0.46.0
## [46] vctrs_0.6.5 Matrix_1.7-1 jsonlite_1.8.9
## [49] bookdown_0.41 hms_1.1.3 bit64_4.5.2
## [52] limma_3.62.0 jquerylib_0.1.4 tidyr_1.3.1
## [55] glue_1.8.0 BiocVersion_3.20.0 UCSC.utils_1.2.0
## [58] tibble_3.2.1 pillar_1.9.0 rappdirs_0.3.3
## [61] htmltools_0.5.8.1 GenomeInfoDbData_1.2.13 R6_2.5.1
## [64] evaluate_1.0.1 lattice_0.22-6 readr_2.1.5
## [67] rentrez_1.2.3 png_0.1-8 memoise_2.0.1
## [70] bslib_0.8.0 SparseArray_1.6.0 xfun_0.48
## [73] pkgconfig_2.0.3